The present invention relates to image quality assessment, and more particularly, to human vision model guided image quality assessment in medical images.
Image quality assessment is an important and challenging problem in image processing. With the rapid advancements of digital image processing, the demand of objective image quality assessment is very high. For example, for image compression, an objective image quality metric that measures the quality of a compressed image can be used to find the best compromise between the rate and distortion in the image compression. Image quality evaluation can also be applied to image communication. When transmitting an image progressively, an objective image quality metric can be used to determine if the transmitted image information is good enough, or if more information needs to be transmitted. Furthermore, image quality assessment can also play an important role in image restoration techniques, such as, structure edge enhancement, noise reduction, image de-blurring, etc. Image quality assessment can also be used to automatically determine the optimal parameters for image filters and to provide search directions in optimization algorithms.
Along with the rapid development of radiology and digital medical imaging systems, image processing and analysis techniques have been developed to increase the quality of medical images. An objective measurement of image quality is especially important for medical images, since the quality of a medical image is directly related to the diagnostic accuracy of the medical image. Some techniques for traditional image quality evaluation can be directly applied for medical image quality assessment. However, because of the differences is the users, image formats, and characteristics of image contents in medical images, directly applying existing natural image quality evaluation methods for evaluating the image quality of medical images does not typically provide satisfactory results. The users of medical imaging systems are typically physicians. Unlike general users for natural images, physicians typically focus on particular regions of interest (ROI) in a medical image, and the quality of other regions in the image may be irrelevant for diagnostic accuracy. The formats and characteristics of medical images also make the problem of medical image quality assessment quite different from that of natural images. Most medical images are of high dynamic ranges, i.e., each pixel is represented by more than 8 bits, while only 8-bit intensity can be displayed on typical monitors. These differences need to be considered in the development of a medical image quality assessment method.
Most conventional medical image quality assessment methods were developed for evaluating the quality of compressed medical images based on full-reference quality metrics, i.e., evaluating the difference between the original image and the distorted (compressed) image. However, the original images may not be available or reliable in many situations. For example, parameter tuning of radiographic machines and filter parameter optimization in medical image post-processing can benefit from objective image quality assessment, but the original image information is not available in these applications. Thus, a no-reference objective quality assessment method for medical images is desirable. Furthermore, research has shown that traditional quantitative image quality metrics, such as peak signal to noise ratio (PSNR) and mean squared error (MSE), are not directly related to human perception. Thus, an objective quality assessment method that takes the human vision system (HVS) into account to accurately reflect the human perception of image quality is desirable.